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This study aims to identify the key determinants of carbon emissions disclosure from an environmental, social and governance (ESG) perspective, offering insights into how these factors influence corporate transparency and sustainability practices.

This study uses H2O Automated machine learning (AutoML), a sophisticated machine learning framework, to analyze CO₂ emissions disclosure among 77 French nonfinancial companies listed on the SBF 120 index between 2017 and 2021. This investigation robustly evaluates CO₂ emission disclosures based on the Carbon Disclosure Project Index criteria. This approach enhances the accuracy of the findings and pioneers a new path in ESG research, blending sophisticated computational tools with traditional environmental reporting metrics.

The study shows an optimal balance between model complexity and accuracy, with social factors and the book market being more influential in CO₂ disclosure than direct environmental factors. The heatmap analysis revealed the significance of these variables in predicting CO₂ disclosures.

This research provides insights for firms and policymakers to improve environmental transparency and reporting, emphasizing the importance of considering ESG aspects. Carbon emissions disclosure is crucial for sustainability, ensuring regulatory compliance, attracting investors and improving risk management.

This research introduces a cutting-edge methodology for analyzing CO₂ emissions disclosure, applying the H2O AutoML framework specifically to French nonfinancial companies listed on the SBF 120 index. This unique application within the French regulatory context, combined with a focus on ESG factors, sets this study apart from previous research. By emphasizing model diversity and the integration of multiple advanced algorithms, the approach provides a more nuanced understanding of environmental disclosure, offering novel insights that can guide policymakers and businesses in enhancing transparency and sustainability practices.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.